This application for a Mentored Patient-Oriented Research Career Development Award entitled Magnetic Resonance Imaging for Global Atherosclerosis Risk Assessment is being submitted by Janet Wei, MD, to continue her professional development as a translational researcher in the inter-disciplinary field of preventive cardiology and magnetic resonance imaging (MRI) measures of atherosclerosis. Cardiovascular disease (CVD) remains the leading cause of death in the United States. Secondary prevention strategies reduce but do not eliminate the risk of recurrent CVD events and many patients are treated to reduce one event. Current risk prediction is not personalized to an individual patient's atherosclerotic burden or adverse plaque features, which have now been shown to have prognostic value. Current measures of atherosclerosis burden and adverse plaque features in individual vascular beds improve prediction of CVD events over traditional risk factors, but their clinical application is not developed for risk prediction of recurrent evens or for therapeutic response monitoring. Novel techniques have been developed for assessing coronary, carotid, and femoral atherosclerosis using MRI methods that are rapid and reproducible, have improved spatial resolution, and do not require contrast media, making atherosclerosis assessment in multiple vascular beds feasible. Because the coronary vascular bed has greater risk assessment value for cardiac events than the carotid and femoral vascular beds, Dr. Wei hypothesizes that global MRI measures of plaque burden and adverse plaque features using all three vascular beds will improve CVD risk discrimination compared to currently available individual vascular bed measures and may be useful to monitor treatment response. The proposed project builds on Dr. Wei's strengths in general clinical cardiology and basic cardiovascular imaging, and she will receive additional training in [1] CVD prevention and epidemiology, [2] MRI quantification and characterization of atherosclerotic plaque, and [3] advanced biostatistics and research methodology for risk prediction. She anticipates that this training will give her the tools to apply MRI atherosclerosis models in clinical trials for the development of personalized CVD prevention and treatment strategies.